Speaker verification using mixture decomposition discrimination

Abstract

A new approach for speaker verification is presented in this paper. Mixture decomposition discrimination (MDD) is based on the idea that, when modeling speech using speaker independent continuous density hidden Markov models (HMM), different speakers speaking the same word would cause different HMM mixture components to dominate. When the mixture information is considered, one can construct a "mixture profile" of a speaker speaking a given word or phrase. This mixture profile is incorporated into a discriminative training procedure to discriminate between a true speaker and all other speakers (or imposters). The effectiveness of MDD is seen when it is incorporated into a hybrid verification system that also includes speaker dependent HMM modeling with cohort normalization. Experimental results show that the hybrid system reduces the average equal error rate (EER) by 46% when compared with the EER of the speaker-dependent HMM verifier. It is also shown that the computational and model storage requirements needed to incorporate MDD into the hybrid system are relatively small.